Create app.py

app.py

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+# app.py
+import base64
+import cv2
+import numpy as np
+import requests
+from fastapi import FastAPI
+from pydantic import BaseModel
+import insightface
+
+# Load Face Detector + Recognition Model (first import may download weights)
+model = insightface.app.FaceAnalysis(name="buffalo_l")
+model.prepare(ctx_id=0, det_size=(640, 640))
+
+app = FastAPI(title="Face Compare API")
+
+class CompareRequest(BaseModel):
+    image1: str | None = None     # base64
+    image2: str | None = None     # base64
+    image1_url: str | None = None # URL
+    image2_url: str | None = None # URL
+
+def b64_to_img(b64_string: str):
+    try:
+        img_data = base64.b64decode(b64_string)
+        arr = np.frombuffer(img_data, np.uint8)
+        img = cv2.imdecode(arr, cv2.IMREAD_COLOR)
+        return img
+    except Exception:
+        return None
+
+def url_to_img(url: str):
+    try:
+        resp = requests.get(url, timeout=10)
+        arr = np.frombuffer(resp.content, np.uint8)
+        img = cv2.imdecode(arr, cv2.IMREAD_COLOR)
+        return img
+    except Exception:
+        return None
+
+def get_embedding(img):
+    faces = model.get(img)
+    if len(faces) == 0:
+        return None
+    return faces[0].embedding
+
+@app.post("/compare")
+async def compare_faces(req: CompareRequest):
+    # Load images (prefer raw base64, else url)
+    img1 = b64_to_img(req.image1) if req.image1 else (url_to_img(req.image1_url) if req.image1_url else None)
+    img2 = b64_to_img(req.image2) if req.image2 else (url_to_img(req.image2_url) if req.image2_url else None)
+
+    if img1 is None or img2 is None:
+        return {"error": "Invalid image data or URL."}
+
+    emb1 = get_embedding(img1)
+    emb2 = get_embedding(img2)
+
+    if emb1 is None or emb2 is None:
+        return {"error": "No face detected in one or both images."}
+
+    # cosine similarity
+    similarity = float(np.dot(emb1, emb2) / (np.linalg.norm(emb1) * np.linalg.norm(emb2)))
+    matched = similarity > 0.55
+
+    return {"similarity": similarity, "match": matched}